dc.contributor.author |
Iftikhar, Sadaf |
|
dc.contributor.author |
Shahid, Saman |
|
dc.contributor.author |
Umar Hassan, Muhammad |
|
dc.contributor.author |
Ghias, Mamoona |
|
dc.date.accessioned |
2022-10-12T10:23:07Z |
|
dc.date.available |
2022-10-12T10:23:07Z |
|
dc.date.issued |
2020-09-04 |
|
dc.identifier.citation |
Iftikhar, S., Shahid, S., Hassan, M. U., & Ghias, M. (2020). Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI). Pakistan Journal of Pharmaceutical Sciences, 33. |
en_US |
dc.identifier.issn |
1011-601X |
|
dc.identifier.uri |
http://142.54.178.187:9060/xmlui/handle/123456789/13055 |
|
dc.description.abstract |
This study aimed to diagnose the incidence of restless leg syndrome (RLS) in patients with diabetes mellitus (DM) type-2, thorough artificial intelligence based multilayer perceptron (MLP). 300 cases of diabetes mellitus type-2, of age between 18-80 years were included. Point-biserial correlation/Pearson Chi-Square correlations were conducted between RLS and risk factors. We trained a backpropagation MLP via. supervised learning algorithm to predict clinical outcome for RLS. Majority of the patients were having hypertension (63%) and with peripheral neuropathy (69%). Two mostly reported scaled parameters were: 18% ‘tiredness’ and 14%, ‘impact on mood’. A significant correlation was found in RLS with smoking, hypertension and chronic renal failure (CRF). MLP model achieved more than 95% accuracy in predicting the outcome with cross entropy error 0.5%. Following scaled symptomatic variables: ‘need/urge to move’ (100%) achieved the highest normalized importance, followed by ‘relief by moving’ (85.7%), ‘sleep disturbance’ (62%) and ‘impact on mood’ (51.3%). Artificial intelligence based models can help physicians to identify the pre diagnose RLS, so that active measures can be taken in time to avoid further complications. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Karachi:Pakistan Journal of Pharmaceutical Sciences, university of Karachi. |
en_US |
dc.subject |
Restless leg syndrome |
en_US |
dc.subject |
multilayer perceptron |
en_US |
dc.subject |
diabetes mellitus |
en_US |
dc.subject |
sleep disturbance |
en_US |
dc.subject |
urge to move |
en_US |
dc.title |
Assessment and prediction of restless leg syndrome (RLS) in patients with diabetes mellitus type II through artificial intelligence (AI) |
en_US |
dc.type |
Article |
en_US |